#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim, l2_loss
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
from torchvision.utils import save_image
from tqdm import tqdm
import numpy as np
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
try:
    from torch.utils.tensorboard import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"


def training(dataset: ModelParams, opt: OptimizationParams, pipe: PipelineParams, args: ArgumentParser):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset, args.type)
    # 创建 ‘GaussianModel’模型，给点云中的每个点创建一个 3D Gaussian
    gaussians = GaussianModel(dataset, args)
    # 加载数据集和每张图片对应的 camera 的参数
    scene = Scene(dataset, gaussians, resolution_scales=args.ratio)
    
    # 加载模型参数
    
    if args.checkpoint:
        print("Create Gaussians from checkpoint {}".format(args.checkpoint))
        scene.load(args.checkpoint)
    gaussians.training_setup(opt, args.type)

    # 设置背景颜色并放置 cuda 上
    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
    
    iter_start = torch.cuda.Event(enable_timing = True)
    iter_end = torch.cuda.Event(enable_timing = True)

    if args.type == 'single_res':
        viewpoint_stack = scene.getTrainCameras().copy()
    elif args.type == 'mutil_res':
        viewpoint_stack = []
        ratios = np.linspace(args.ratio[0], args.ratio[-1], 100)
        for ratio in ratios:
            viewpoint_stack += scene.getTrainCameras(ratio).copy()
                        
    viewpoint_indexs = None
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    for iteration in range(first_iter, opt.iterations + 1):        
        iter_start.record()

        # 更新 xyz 的学习率
        gaussians.update_learning_rate(iteration)

        # Every 1000 its we increase the levels of SH up to a maximum degree
        # 每迭代 1000 轮次将球谐的阶数 +1，直到达到最大阶数
        if iteration % 1000 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        # 随机选择一个图片和对应的视角参数（内外参）
        if not viewpoint_indexs:
            viewpoint_indexs = list(range(len(viewpoint_stack)))
        index = viewpoint_indexs.pop(randint(0, len(viewpoint_indexs)-1))
        viewpoint_cam = viewpoint_stack[index]
                        
        # Render
        if (iteration - 1) == args.debug_from:
            pipe.debug = True
            
        # 根据 3D Gaussian 渲染该相机视角下的图像
        render_pkg = render(viewpoint_cam, gaussians, pipe, background, transfer=(args.type=='mutil_res'))
        
        image = render_pkg["render"]
        viewspace_point_tensor = render_pkg["viewspace_points"]
        visibility_filter = render_pkg["visibility_filter"]
        radii = render_pkg["radii"]
        
        # Loss
        # 在渲染图像和 GT 图像之间计算 loss
        gt_image = viewpoint_cam.original_image.cuda()
        Ll1 = l1_loss(image, gt_image)
        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
        loss.backward()
        gaussians.optimizer.step()
        gaussians.optimizer.zero_grad()
        
        iter_end.record()
        
        if iteration % args.vis_interval == 0:
            save_dir = os.path.join(dataset.model_path, 'vis')
            os.makedirs(save_dir, exist_ok=True)
            rendered_cat = torch.cat([gt_image, image], -1)
            save_image(rendered_cat, os.path.join(save_dir, f'{iteration:05d}-{viewpoint_cam.uid}.png'))
        
        with torch.no_grad():
            # Progress bar                
            if iteration % 10 == 0:
                psnr_ = psnr(image, gt_image).mean()
                progress_bar.set_postfix({"PSNR": f"{psnr_:.4f}", "Pts": f"{gaussians.get_xyz.shape[0]}"})
                tb_writer.add_scalar(f'train/ratio={viewpoint_cam.ratio}', psnr_, iteration)
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log and save
            if iteration % args.save_interval == 0 or (iteration == opt.iterations): 
                scene.save(iteration)
            if iteration % args.test_interval == 0:
                training_report(args, iteration, scene, render, pipe, background, tb_writer)

            # Densification
            # 对 3D Gaussian 进行稠密化
            if iteration < opt.densify_until_iter and args.type == "single_res":
            # if iteration < opt.densify_until_iter:
                gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)

                if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
                    size_threshold = 20 if iteration > opt.opacity_reset_interval else None
                    gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
                
                if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                    gaussians.reset_opacity()
            del image, gt_image
            torch.cuda.empty_cache()


def prepare_output_and_logger(args, type):        
    if not args.model_path:
        args.scene_name = args.source_path.split('/')[-1]
        args.model_path = os.path.join("./output/", args.scene_name, type)
        
    # Set up output folder
    print("Output folder: {}".format(args.model_path))
    os.makedirs(args.model_path, exist_ok = True)
    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
        cfg_log_f.write(str(Namespace(**vars(args))))

    # Create Tensorboard writer
    tb_writer = None
    if TENSORBOARD_FOUND:
        tb_writer = SummaryWriter(args.model_path)
    else:
        print("Tensorboard not available: not logging progress")
    return tb_writer

def training_report(args, iteration, scene: Scene, renderFunc: render, pipe, background, tb_writer):
    save_path = os.path.join(scene.model_path, 'eval')
    os.makedirs(save_path, exist_ok=True)
    for ratio in args.ratio:
        test_cameras = scene.getTestCameras(ratio)
        psnr_test = 0.0
        t_psnr_test = 0.0
        for idx, viewpoint in enumerate(test_cameras):
            t_image = torch.clamp(renderFunc(viewpoint, scene.gaussians, pipe, background, transfer=True)["render"], 0.0, 1.0)
            image = torch.clamp(renderFunc(viewpoint, scene.gaussians, pipe, background, transfer=False)["render"], 0.0, 1.0)
            gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
            t_psnr_test += psnr(t_image, gt_image).mean().double()
            psnr_test += psnr(image, gt_image).mean().double()
            
            if idx < 2:
                img = torch.cat([image, t_image, gt_image], -1)
                save_image(img, os.path.join(save_path, f"{iteration}-{str(ratio)[0]}.png"))
            
        t_psnr_test /= len(test_cameras)
        psnr_test /= len(test_cameras)
        print(f"\n[ITER {iteration}] Ratio {ratio}, Test: PSNR {psnr_test}, T_PSNR {t_psnr_test}")
        tb_writer.add_scalar(f'test/ratio={ratio}', t_psnr_test, iteration)
        del t_image, image, gt_image
    del test_cameras
    torch.cuda.empty_cache()

if __name__ == "__main__":
    # Set up command line argument parser
    parser = ArgumentParser(description="Training script parameters")
    lp = ModelParams(parser)
    op = OptimizationParams(parser)
    pp = PipelineParams(parser)
    parser.add_argument('--ip', type=str, default="127.0.0.1")
    parser.add_argument('--port', type=int, default=6009)
    parser.add_argument('--debug_from', type=int, default=-1)
    parser.add_argument('--detect_anomaly', action='store_true', default=False)
    parser.add_argument("--test_interval", type=int, default=10000)
    parser.add_argument("--save_interval", type=int, default=10000)
    parser.add_argument("--vis_interval", type=int, default=1000)
    parser.add_argument("--quiet", action="store_true")
    parser.add_argument("--checkpoint", type=str, default="")
    parser.add_argument("--gpu", type=int, default=0)
    parser.add_argument("--ratio", nargs='+', type=float)
    parser.add_argument("--type", type=str, default="single_res", choices=['single_res', 'mutil_res'])
    args = parser.parse_args(sys.argv[1:])
    
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)
    
    torch.cuda.set_device(args.gpu)
    
    # Start GUI server, configure and run training
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    
    training(lp.extract(args), 
             op.extract(args), 
             pp.extract(args), 
             args)

    # All done
    print("\nTraining complete.")
